What Vector database means in practice
A vector database stores high-dimensional vectors (embeddings) and serves nearest-neighbour queries with sub-100ms latency at enterprise scale. For sovereign deployments MindMap uses pgvector under 10M chunks (operational simplicity — one fewer system to back up), Qdrant for 10–100M chunks (faster snapshot/restore, better payload filtering), and Milvus beyond 100M chunks or where GPU-accelerated indexing matters. We deliberately avoid Pinecone, Weaviate Cloud and Chroma Cloud — they don't ship as on-prem and therefore don't meet the sovereign requirement. The choice between the three open-source options is operational not architectural; pick on operability not micro-benchmark performance.
Related terms
Embedding →
A dense numeric vector representation of a piece of text (or image, or audio) such that semantically similar inputs produce vectors close in the embedding space.
Retrieval-Augmented Generation (RAG) →
A pattern where, instead of relying solely on the LLM's training, the system retrieves relevant documents from a knowledge base and includes them in the prompt as context.
Hybrid retrieval →
Combining dense vector search with sparse keyword search (BM25), then fusing the results — typically yielding 15–25% accuracy lift over either alone.
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All 62 terms, in plain language
Sovereign AI, RAG, agentic AI, IDP, MLOps and the regulations that shape enterprise AI.